Temporal Coverage Bias in Financial Panel Data: A Coverage-Aware Structuring Framework with Evidence from the Dhaka Stock Exchange

TL;DR

This paper identifies and formalizes the problem of temporal coverage bias in financial panel data, demonstrating significant distortions in return dynamics and volatility when naive calendar alignment is used, and proposes a coverage-aware structuring framework.

Contribution

It introduces a novel coverage-aware structuring framework that accounts for heterogeneous entry dates in panel data, reducing distortions in financial and other heterogeneous panels.

Findings

Forward-filling suppresses return volatility by roughly 20% on average.

GARCH unconditional variance distortions exceed 26% in over 90% of instruments.

Naive calendar alignment causes significant distortions in return dynamics and volatility.

Abstract

A common practice in empirical finance is to construct calendar-aligned panels that implicitly treat all instruments as having existed for the full observation period. When securities with different listing histories are combined without explicit coverage constraints, price histories can be inadvertently extended before valid trading ever began. This paper formalizes this problem and proposes a coverage-aware structuring framework built around instrument-level observation windows encoded through structured metadata and an availability matrix. Applied to end-of-day data from the Dhaka Stock Exchange spanning October 2012 to January 2026 and covering 486 instruments, the framework reveals substantial distortions from naive temporal alignment. ARIMA-based experiments establish the mechanism through which padded observations corrupt return dynamics, and volatility analysis across 53 instruments shows that forward-filling alone suppresses return volatility by roughly 20% on average, with GARCH unconditional variance distortions exceeding 26% in over 90% of instruments - a lower bound, as backward extension to the panel start produces 36.6% suppression and causes GARCH non-convergence in 41% of instruments. The distortion affects any method requiring calendar alignment of heterogeneous histories, including dynamic time warping, covariance-based portfolio construction, factor model regression, and temporal foundation model fine-tuning. Although demonstrated on financial data, the framework applies to any panel combining entities with heterogeneous entry dates, including sensor networks, clinical cohorts, and country-level economic panels. Listing coverage is not a minor preprocessing detail but a first-order variable in panel construction.